Fine-Grained Cache Techniques

Updated 22 November 2025

Fine-Grained Cache is a memory management strategy characterized by sub-block level control over placement, eviction, and coherence to boost efficiency.
It leverages formal I/O models and hardware innovations, such as in-DRAM caches and dynamic CPU partitioning, to optimize performance for diverse workloads.
Applications in LLM inference, graph processing, and data systems demonstrate its ability to reduce redundancy, improve throughput, and conserve energy.

A fine-grained cache is a memory hierarchy, cache management strategy, or algorithmic technique that provides selective, high-resolution control over cache placement, eviction, coherence, or utilization at sub-block, per-record, per-segment, or per-computation node granularity. Fine-grained caches are distinguished from coarse-grained alternatives by their ability to expose, measure, or act upon cache state and access patterns at the smallest levels relevant to performance and efficiency constraints of modern hardware, data-intensive workloads, or algorithmic I/O bounds. Techniques span domains from in-memory data stores, database fragments, in-DRAM or GPU caches, distributed neural network KV caches, to graph processing accelerators and fine-tuned LLM attention. Fine-grained caching aims to minimize redundancy, contention, and bandwidth waste, while maximizing utilization, performance isolation, data locality, and throughput.

1. Theoretical Foundations: Cache Models and I/O Complexity

The formal paper of fine-grained cache behavior is grounded in models like the two-level memory or external-memory I/O (cache-miss) model, in which a fast cache of size $M$ interacts with an unbounded slow memory, data moves in blocks (size $B$ ), and algorithmic complexity is measured in the number of cache misses as a function of $n$ , $M$ , and $B$ (Demaine et al., 2017). The red-blue pebble game formalizes optimal cache utilization for arbitrary DAG computations: red pebbles represent cache, blue slow memory, and the aim is to minimize Input/Output operations (cache loads/evictions) given $M$ (Li et al., 12 Oct 2024).

Tight I/O lower and upper bounds—for example, $\Theta(n^2 d^2/M)$ for softmax attention forward passes and $\Theta((n^2 d^2 + n d^3)/M)$ for the backward pass when $M = \Omega(d^2)$ —characterize the fundamental regime in which fine-grained cache management becomes essential. When $M$ is small (e.g., $M = o(d^2)$ ), optimized tiling (block size $B \approx \sqrt{M}$ ) and blocking algorithms achieve $\Theta((n^2 d + n d^2)/\sqrt{M})$ I/O (Li et al., 12 Oct 2024). These bounds motivate cache architectures and evictions that exploit fine-grained data reuse and access locality.

2. Fine-Grained Caching in Systems and Architectures

a) In-DRAM and Hardware-Level Fine-Grained Caches

Fine-grained in-DRAM caches address the inefficiency of row- or coarse-grained copy: FIGCache, built on the FIGARO substrate, enables intra-bank relocations at cache-block (e.g., 64 B) granularity using shared global row buffers and a new RELOC command, achieving distance-independent block migrations with minimal hardware overhead (<1% chip area) (Wang et al., 2020). Benefit counters, block-level tags, and selective migration maximize utilization, row-buffer hits, and overall memory system throughput (16.3% speedup for 8-core workloads, 7.8% DRAM energy reduction).

b) CPU Cache Partitioning and VM-level Abstraction

In CPUs, fine-grained partitioning addresses multi-tenancy and dynamic phase behavior. Com-CAS employs compiler-guided probes to predict per-loop-nest cache requirements, enabling just-in-time LLC way reallocation via Intel CAT at loop-entry granularity, keeping per-application SLA within 15% of baseline latency and increasing throughput up to 35% for heavy mixes (Chatterjee et al., 2021). CacheX derives fine-grained abstractions inside VMs using software-only eviction-set probing, supporting contention-aware scheduling and virtual-color-aware page cache management, yielding up to 25% throughput and 10% latency improvements (Tofigh et al., 13 Nov 2025).

3. Fine-Grained Cache Management in Large Model Inference and Dataflows

a) Deep Learning Attention and KV Cache Optimization

LLM inference and training with long contexts incur prohibitive cache and bandwidth costs due to quadratic attention. Fine-grained cache management strategies—such as MaskKV's per-head-token adaptive eviction based on mask-query attention scores, and layer/headwise dynamic budgets—trim KV caches to <5% of original size with 94.3% quality retention, scaling to 31 $\times$ speedup on 32k-token denoising tasks (Huang et al., 10 Oct 2025).

OmniSparse uses three fine-grained mechanisms for long-video multimodal attention: query selection (active/lazy classification), head-level dynamic KV budgeting (based on attention mass/kurtosis), and headwise KV cache slimming (demodality-based fetch), yielding up to 2.7 $\times$ speedup and 2.4 $\times$ memory reduction in visual-LM prefill and decode (Chen et al., 15 Nov 2025).

b) Distributed and Segment-Level Caching in LLM Serving

TokenLake exposes a declarative, segment-level cache pool API for elastic prefix caching of LLMs, decoupling cache management from scheduling and allowing segment-wise deduplication, balanced load, and distributed cache placement. Heavy-hitter segments are adaptively replicated by measured access frequency to avoid hot spots. The pooled, fine-grained model yields up to 2.6 $\times$ –5.5 $\times$ throughput and 2 $\times$ –2.1 $\times$ hit-rate over conventional tightly-coupled cache-aware routing or per-instance solutions, with cache load CV $\sim$ 11% (Wu et al., 24 Aug 2025).

Hybrid-grained approaches, as in controllable diffusion generation, pair coarse (block-wise) and fine-grained (prompt-level/cross-attention map) caches to exploit intra-stage convergence: once attention outputs stabilize, prompt-level cache reuses cross-attention maps for later steps, yielding substantial MAC reductions (up to 63%) and <1.5% semantic fidelity loss (Liu et al., 14 Nov 2025).

4. Fine-Grained Cache Microarchitectures for Irregular Workloads

Graph analytics and other memory-bound applications with sub-word data elements (e.g., 8 B vs. 64 B DDR bursts) require microarchitectures and DRAM interfaces capable of fine-grained random access. Piccolo introduces a "Piccolo-Cache" with 8 B sector granularity and compact fg-tags, coupled with Piccolo-FIM in-DRAM primitives for function-in-memory random scatter–gather at the sector level. This eliminates up to 87.5% bandwidth waste, boosts cache hit rates, and increases speedup by up to 3.28 $\times$ (1.62 $\times$ geo. mean) across graph workloads, at marginal area and energy cost (Shin et al., 7 Mar 2025).

5. Fine-Grained Caches in Data Systems and Security Context

a) Data Stores and Relational Caches

SQLcached implements fine-grained in-memory caching at the record and field level, allowing relational storage, update, retrieval, and expiry of complex objects without coarse-grained value serialization. Fine-grained expiry and invalidation, by SQL WHERE predicates or index lookups, enables targeted cache invalidation (<1 ms for single page/user data expiration) and reduces peak load compared to namespace-wide flushes (0910.0187).

b) Fine-Grained Cache Occupancy Attacks

Reverse engineering of the Apple M1 SLC reveals exclusivity policies (for CPU) and high associativity that attackers leverage for fine-grained occupancy monitoring. Side channels achieve fine spatial (57 row) and temporal (frame-cycle) granularity for pixel stealing and generalized screen-capture from SLC activity, with 90–94% accuracy, exposing the need for fine-grained countermeasures (e.g., mask-flooding, hardware noise) (Xu et al., 18 Apr 2025).

6. Implementation Guidelines and Trade-offs

For matrix computations and LLM attention, optimal tiling and blocking must track cache size $M$ , with $\sqrt{M}\times\sqrt{M}$ blocking for small $M$ , larger row-blocks for $M=\Omega(d^2)$ (Li et al., 12 Oct 2024).
In software caches (e.g., SQLcached), schema design and index selection balance fine-grained access performance against RAM overhead (0910.0187).
For VM-level management, per-set and per-color metrics must be periodically recalibrated (to counter memory fragmentation and hidden mappings) (Tofigh et al., 13 Nov 2025).
Hardware fine-grained caches (e.g., FIGCache) trade off tag store size, controller overhead, and complexity of benefit tracking against achievable hit rates and migration latency (Wang et al., 2020).
In cluster-scale distributed KV caching (e.g., TokenLake), segment size C set by Roofline analysis avoids over-amortizing communication, and heavy-hitter segment replication is capped to $O(N\log N)$ (Wu et al., 24 Aug 2025).

7. Future Directions and Limitations

Dynamic adaptation of segment/cache block size according to workload or resource constraints remains an open challenge; current deployments often use a fixed size (e.g., TokenLake's $C\approx 568$ tokens) (Wu et al., 24 Aug 2025).
Many hardware techniques require architectural support (e.g., new DRAM commands or controller features) or substantial changes to software/hypervisors for feedback-driven LLC management (Wang et al., 2020, Tofigh et al., 13 Nov 2025).
Workloads with very high spatial locality may see diminishing returns from fine-grained caching; detection and fallback logic are necessary for efficient heterogeneity (Shin et al., 7 Mar 2025).
Security attacks exploiting fine-grained cache occupancy highlight the dual-use nature of such detailed cache visibility, necessitating careful balance between performance and isolation guarantees (Xu et al., 18 Apr 2025).

In summary, fine-grained cache techniques are indispensable for extracting maximal efficiency from hierarchical memory systems in the presence of irregular access patterns, concurrency, and tight performance constraints. They are founded on formal complexity models, advanced hardware primitives, and adaptive software logic, providing both the analytical footing and practical toolkit for future systems that must balance flexibility, security, and speed.